Systems and methods of connected driving based on dynamic contextual factors

ABSTRACT

Systems including one or more sensors, coupled to a vehicle, may detect sensor information and provide the sensor information to another computing device for processing. A system includes one or more sensors, coupled to a vehicle and configured to detect sensor information, and a computing device configured to communicate with one or more mobile sensors to receive the mobile sensor information, communicate with the one or more sensors to receive the sensor information, and analyze the sensor information and the mobile sensor information to identify one or more risk factors.

This application claims priority to U.S. Provisional Patent Application62/895,390, filed Sep. 3, 2019, and U.S. Provisional Patent Application63/043,561 filed Jun. 24, 2020, the contents of each of which areincorporated herein by reference in their entirety.

BACKGROUND

Recently, many vehicles come equipped with global positioning system(GPS) devices that help drivers to navigate roads to various locations.Moreover, many drivers use other mobile devices (e.g., smart phones)that have GPS devices therein to help the drivers navigate roads. TheseGPS devices may provide location information and use maps for navigationpurposes. As GPS devices have become more prevalent, the different usesfor their location information have come to light. In some instances,the danger level of different routes is determined by combining locationinformation and accident history information. Although some entities mayfind the danger level of certain routes useful and interesting, suchinformation alone might not significantly reduce the likelihood ofaccidents occurring. Therefore, there remains a desire for methods andsystems that may help drivers avoid accidents. Moreover, in the event ofan accident, there is a desire for methods and systems that utilizeinformation regarding the environment in which the accident occurred tohelp other drivers avoid a similar accident.

SUMMARY

In light of the foregoing background, the following presents asimplified summary of the present disclosure in order to provide a basicunderstanding of some aspects of the disclosure. This summary is not anextensive overview of the disclosure. It is not intended to identify keyor critical elements of the disclosure or to delineate the scope of thedisclosure. The following summary merely presents some concepts of thedisclosure in a simplified form as a prelude to the more detaileddescription provided below.

Certain other aspects of the disclosure include a system comprising oneor more sensors coupled to a vehicle and configured to detect sensorinformation. One aspect of the present disclosure relates to a systemconfigured for mobile data and vehicle communication. The system mayinclude one or more hardware processors configured by machine-readableinstructions. The processor(s) may be configured to provide dynamiccomputer readable recommendations to users to identify and mitigate riskduring a driving operation and traveling with the mobile device. Thesystem may receive various types of data information, including but notlimited to, accident information, geographic information, environmentalinformation, risk information, and vehicle information from one or moresensors.

The system may calculate a dynamic risk assessment and associate therisk assessment to a particular mobile device. The system may includeone or more hardware processors configured by machine-readableinstructions. The processor(s) may be configured to calculate a dynamicrisk assessment with mobile device derived input data to determine oneor more contextual risk assessments from a range of mobile devicederived inputs to calculate one or more indexes, such as a tailgatingrisk index, a road frustration index, a lane keeping index, a driverattention index, an internal distraction index and/or other risk index.Further, the processor(s) may be configured to provide alerts to a userby indicating an identification of a risk alert based on the calculatedrisk assessment. The processor(s) may be configured to create a risk mapthat permits a location and contextual (e.g., reflecting traffic and/orweather conditions) assessment of risks. Among other things, thisassessment of risks can be used to calculate the safest route, andrestrict certain actions (e.g., enable/disable functionality on a mobiledevice) when conditions are safe/not safe.

In other aspects of the disclosure, a system including one or moresensors, coupled to and in communication with a vehicle, may detectsensor information and provide the sensor information to a computingdevice for processing. The computing device may be configured tocommunicate with one or more mobile sensors to receive the mobile sensorinformation, communicate with the one or more sensors to receive thesensor information; and analyze the sensor information and the mobilesensor information to identify one or more risk factors.

In other aspects of the disclosure, a personal navigation device, mobiledevice, and/or personal computing device may access a database of riskscores to assist in identifying and indicating alternate lower-risktravel routes.

Aspects of the disclosure may enable consumers to maintain a connectedlife in a safe manner when they are in the vehicle. Systems and methodsdescribed herein may monitor and derive an assessment of the risks thata motorist faces during vehicle operation, and the quantifiable riskthat they are facing, and predicted risk in the very near-term future.In other aspects, the systems and methods described herein may help adriver to determine which activities in the connected vehicle they takecan undertake with various operations of the mobile device of the user.In some aspects, the systems and methods described herein may makerecommendations on which mobile device user activities could be deferredfor a threshold period of time or performed at a safer time. In someimplementations, the recommendations may include one or more of audio,video, or static image recommendations; or the recommendations mayinclude enabling or disabling features on the mobile device of the user.

Certain other aspects of the disclosure may include a system comprisingone or more sensors coupled to a vehicle and configured to detect sensorinformation. Aspects of the disclosure relate to methods,computer-readable media, and apparatuses for analyzing vehicle operationdata, or driving data, and calculating or adjusting a driver score basedon the analyzed driving data. One or more computing devices within avehicle, for example, a mobile device and sensors in the vehicle, may beconfigured to collect vehicle operational data, driver operational dataand transmit the data to a risk assessment server. Vehicle operationaldata may include various data collected by the vehicle's internalsensors, computers, and cameras, such as the vehicle's speed, rates ofacceleration, braking, or steering, impacts to the vehicle, usage ofvehicle controls, and other vehicle operational data.

The details of these and other aspects of the disclosure are set forthin the accompanying drawings and descriptions below. Other features andadvantages of aspects of the disclosure may be apparent from thedescriptions and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features, aspects, and advantages of the presentdisclosure will become better understood with regard to the followingdescription, claims, and drawings. The present disclosure is illustratedby way of example, and not limited by, the accompanying figures in whichlike numerals indicate similar elements.

FIG. 1 illustrates an example operating environment in accordance withaspects of the present disclosure.

FIG. 2 depicts an example of sensors coupled to a vehicle in accordancewith aspects of the present disclosure.

FIG. 3 depicts another example operating environment in accordance withaspects of the present disclosure.

FIG. 4 depicts an illustrative user scenario and user interface that maybe displayed in accordance with aspects of the present disclosure.

FIG. 5 depicts an illustrative functional block diagram of a neuralnetwork that may be used to implement the processes and functionsdescribed herein, in accordance with one or more implementations.

FIG. 6 depicts a flowchart of an example process in accordance withaspects of the present disclosure.

FIG. 7 depicts a functional block diagram of a mobile communicationdevice in accordance with aspects of the present disclosure.

FIG. 8 depicts a risk matrix in accordance with aspects of the presentdisclosure.

FIGS. 9A-9C depict illustrative user interfaces in accordance withaspects of the present disclosure.

FIG. 10 depicts a functional block diagram of an example operation inaccordance with aspects of the present disclosure.

FIG. 11 depicts an illustrative user interface in accordance withaspects of the present disclosure.

FIG. 12 depicts an illustrative user interface in accordance withaspects of the present disclosure.

FIG. 13 depicts an illustrative user interface in accordance withaspects of the present disclosure.

DETAILED DESCRIPTION

In accordance with various aspects of the disclosure, methods,non-transitory computer-readable media, and apparatuses are disclosedfor dynamic computer readable recommendations and computer actions toidentify and mitigate risk during a driving operation and traveling withthe mobile device.

FIG. 1 illustrates an example of a suitable computing system 100 thatmay be used according to one or more illustrative embodiments. Thecomputing system 100 is only one example of a suitable computing systemand is not intended to suggest any limitation as to the scope of use orfunctionality contained in the present disclosure. The computing system100 should not be interpreted as having any dependency or requirementrelating to any one or combination of components shown in theillustrative computing system.

The present disclosure is operational with numerous other generalpurpose or special purpose computing systems or configurations. Examplesof computing systems, environments, and/or configurations that may besuitable for use with the disclosed embodiments include, but are notlimited to, personal computers (PCs), server computers, hand-held orlaptop devices, mobile devices, tablets, multiprocessor systems,microprocessor-based systems, set-top boxes, programmable consumerelectronics, network PCs, minicomputers, mainframe computers,distributed computing environments that include any of the above systemsor devices, and the like that are configured to perform the processes,functions, and the like, described herein.

With reference to FIG. 1, the computing system 100 may include acomputing device 101 wherein the processes discussed herein may beimplemented. The computing device 101 may have a processor 103 forcontrolling the overall operation of the random access memory (RAM) 105,read-only memory (ROM) 107, input/output module 109, memory 115, modem127, and local area network (LAN) interface 123. Processor 103 and itsassociated components may allow the computing device 101 to run a seriesof computer readable instructions related to receiving, storing,generating, calculating, identifying, and analyzing data to generate arisk map. Computing system 100 may also include optical scanners (notshown). Exemplary usages include scanning and converting paperdocuments, such as correspondence, data, and the like to digital files.

Computing device 101 may include a variety of computer-readable media.Computer-readable media may be any available media that may be accessedby computing device 101 and include both volatile and non-volatile mediaas well as removable and non-removable media. Computer-readable mediamay be implemented in any method or technology for storage ofinformation such as computer-readable instructions, data structures,program modules, or other data. Computer-readable media include, but arenot limited to, random access memory (RAM), read only memory (ROM),electronically erasable programmable read only memory (EEPROM), flashmemory or other memory technology, or any other medium that can be usedto store desired information that can be accessed by computing device101. For example, computer-readable media may comprise a combination ofcomputer storage media (including non-transitory computer-readablemedia) and communication media.

RAM 105 may include one or more applications representing theapplication data stored in RAM 105 while the computing device 101 is onand corresponding software applications (e.g., software tasks) arerunning on the computing device 101.

Input/output module 109 may include a sensor(s), a keypad, a touchscreen, a microphone, and/or a stylus through which a user of computingdevice 101 may provide input, and may also include a speaker(s) forproviding audio output and a video display device for providing textual,audiovisual, and/or graphical output.

Software may be stored within memory 115 and/or storage to provideinstructions to processor 103 for enabling computing device 101 toperform various functions. For example, memory 115 may store softwareused by the computing device 101, such as an operation system 117,application program(s) 119, and an associated database 121. Also, someor all of the computer-executable instructions for computing device 101may be embodied in hardware or firmware.

Computing device 101 may operate in a networked environment supportingconnections to one or more remote computing devices, such as computingdevices 135, 141, and 151. The computing devices 141 and 151 may bepersonal computing devices, mobile computing devices, or servers thatinclude many or all of the elements described above about the computingdevice 101. The computing device 135 may be a transceiver or sensor thatincludes many or all of the elements described above about computingdevice 101.

The network connections depicted in FIG. 1 include a local area network(LAN) 125 and a wide area network (WAN) 129, but may also includeanother type of network. When used in a LAN networking environment,computing device (e.g. in some instances a server) 101 may be connectedto the LAN 125 through a network interface (e.g. LAN interface 123) oradapter in the communications module 109. When used in a WAN networkingenvironment, the computing device 101 may include a modem 127 or othermeans for establishing communications over the WAN 129, such as theInternet 131 or another type of computer network. It will be appreciatedthat the network connections shown are illustrative, and other means ofestablishing a communications link between the computing devices may beused. Various well-known protocols such as TCP/IP, Ethernet, FTP, HTTPand the like may be used, and the system may be operated in aclient-server configuration to permit a user to retrieve a web page froma web-based server. Further, various conventional web browsers may beused to display and manipulate web pages.

Various aspects described herein may be embodied as a method, a dataprocessing system, or as a computer-readable medium storingcomputer-executable instructions. For example, a computer-readablemedium may store instructions to cause a processor 103 to perform stepsof methods described herein. Such a processor 103 may executecomputer-executable instructions stored on a computer-readable medium.

FIG. 2 illustrates an example system in which sensors 204 a through 204h are coupled to a vehicle 208. In other examples, only a single sensor204 may be used. The sensors 204 may be coupled to or otherwise incommunication with vehicle 208 in the arrangement shown in FIG. 2 or inother various arrangements (not shown). During operation of the vehicle208, a user (e.g., driver, passenger, etc.) of the vehicle 208 may belocated at the position depicted by identifier 202. Sensors 204 (e.g.204 a through 204 h) may be located inside, outside, on the front, onthe rear/back, on the top, on the bottom, and/or on each side of thevehicle 208. In some cases, the number of sensors 204 used andpositioning of the sensors 204 may depend on the vehicle 208, so thatsensor information for all areas surrounding the vehicle 208 may becollected.

The sensor(s) 204 may gather or detect sensor information. The sensorinformation may comprise data that represents the internal and/orexternal surroundings of the vehicle 208. In some examples, the sensorinformation may include data that represents the vehicle 208 itself sothat the vehicle's shape and size may be determined from such data. Thesensor(s) 204 may comprise a light detection and ranging (LIDAR) sensor,a sound navigation and ranging (SONAR) sensor, a video/image recordingsensor, a camera, a light sensor, a thermal sensor, an optical sensor,an acceleration sensor (accelerometer), a vibration sensor, a motionsensor, a position sensor, a technology (e.g., sensing device orscanner) used to sense and detect the characteristics of the sensingdevice's surroundings and/or environment, and the like. In someembodiments, each sensor 204 a through 204 h may be the same type ofsensor. In other embodiments, sensors 204 a through 204 h may comprise acombination of different types of sensors. For example, sensor 204 a maybe a LIDAR sensor, and sensor 204 b may be an optical sensor. In someembodiments, the sensors 204 may be specially designed to combinemultiple technologies (e.g., a sensor 204 may include accelerometer andLIDAR components). In some aspects, the sensor information may be in theform of vectors. The vectors may be labeled or organized based onclassification of each vector. The classification of each vector (and/orsets of vectors) may be generated using a formulaic or machine learningapproach. The information the vector contains, or more generally, thesensor information, may be stored, quantized, or interpreted with otherapproaches, e.g., graph data for semantic inference queries at a laterpoint in time.

The system may gather additional information, such as environmentalinformation, road information, vehicle information, weather information,geographic location information, accident information, etc.Environmental information may comprise data about the surroundings ofthe vehicle 208. In some embodiments, the environmental information maycomprise road, weather, and geographic information. For example,environmental information may comprise data about the type of route thevehicle 208 is traveling along (e.g., if the route is rural, city,residential, etc.). In another example, the environmental informationmay include data identifying the surroundings relative to the road beingtraveled by the vehicle 208 (e.g., animals, businesses, schools, houses,playgrounds, parks, etc.). As another example, the environmentalinformation may include data detailing foot traffic and other types oftraffic (e.g. pedestrians, cyclists, motorcyclists, and the like).

Road information may comprise data about the physical attributes of theroad (e.g., slope, pitch, surface type, grade, and the like). In someaspects, the physical attributes of the road may comprise a pothole(s),a slit(s), an oil slick(s), a speed bump(s), an elevation(s) orunevenness (e.g., if one lane of road is higher than the other, whichoften occurs when road work is being done), etc. In some embodiments,road information may comprise the physical conditions of the road (e.g.,flooded, wet, slick, icy, plowed, not plowed/snow covered, etc.). Insome instances, road information may be data from a sensor that gathersand/or analyzes some, most, or all vertical changes in a road. In otherexamples, road information may include information about characteristicscorresponding to the rules of the road or descriptions of the road:posted speed limit, construction area indicator (e.g., whether locationhas construction), topography type (e.g., flat, rolling hills, steephills, etc.), road type (e.g., residential, interstate, 4-lane separatedhighway, city street, country road, parking lot, etc.), road feature(e.g., intersection, gentle curve, blind curve, bridge, tunnel), numberof intersections, whether a roundabout is present, number of railroadcrossings, whether a passing zone is present, whether a merge ispresent, number of lanes, width of roads/lanes, population density,condition of road (e.g., new, worn, severely damaged with sink-holes,severely damaged by erosion, gravel, dirt, paved, etc.), wildlife area,state, county, and/or municipality. In some embodiments, roadinformation may include data about infrastructure features of the road.For example, infrastructure features may include intersections, bridges,tunnels, railroad crossings, and other roadway features.

Weather information may comprise data about the weather conditionsrelative to a vehicle's 208 location (e.g., snowing, raining, windy,sunny, dusk, dark, etc.). In some aspects, weather information mayinclude a forecast of potential weather conditions for a segment of aroad being traveled by vehicle 208. For example, weather information mayinclude a storm warning, a tornado warning, a flood warning, a hurricanewarning, etc. In some aspects, weather information may provide dataabout road segments affected by weather conditions. For example, weatherinformation may detail which roads are flooded, icy, slick,snow-covered, plowed, or closed. As another example, the weatherinformation may include data about glare, fog, and the like.

Vehicle information may comprise data about how the vehicle 208 isoperated (e.g., driving behavior and/or vehicle operation data). In someembodiments, a vehicle telematics device may be used to gatherinformation about operation of a vehicle. For example, the vehicletelematics device may gather data about the braking, accelerating,speeding, and turning of a vehicle 208. In some aspects, vehicleinformation may comprise accident information (which will be describedlater). For example, vehicle information may include data that describesincidents (e.g., vehicle accidents) and a particular location where theincident occurred (e.g., geographic coordinates associated with a roadsegment, intersection, etc.). In some aspects, vehicle information mayinclude the vehicle make, vehicle model, vehicle year, and the like. Insome instances, vehicle information may comprise data collected throughone or more in-vehicle devices or systems such as an event data recorder(EDR), onboard diagnostic system, or global positioning satellite (GPS)device. Examples of information collected by such devices include speedat impact, brakes applied, throttle position, direction at impact, andthe like. In some examples, vehicle information may also includeinformation about the user (e.g., driver, passenger, and the like)associated with the vehicle 208.

In some aspects, user information may include data about a user's age,gender, marital status, occupation, blood alcohol level, credit score,eyesight (e.g., whether the user wears glasses and/or glassesprescription strength), height, and physical disability or impairment,as provided by the user and as used with appropriate permission of theuser. In some instances, user information may include data about theuser's distance from a destination, route of travel (e.g., startdestination and end destination), and the like. In some embodiments, theuser information may comprise data about the user's non-operationactivities while operating a vehicle 208. For example, the data maycomprise the user's mobile phone usage while operating the vehicle 208(e.g., whether the user was talking on a mobile device, texting on amobile device, searching on the Internet on a mobile device, etc.), thenumber of occupants 1010 in the vehicle 208, the time of day the userwas operating the vehicle 208, etc.

Geographic location information may comprise data about the physicallocation of a vehicle 208. For example, the geographic locationinformation may comprise coordinates with the longitude and latitude ofthe vehicle, or a determination of the closest address to the actuallocation of the vehicle 208. In another example, the vehicle locationdata may comprise trip data indicating a route the vehicle 208 istraveling along. In some aspects, the geographic location informationmay also include information that describes the geographic boundaries,for example, of an intersection (e.g. where vehicle 208 is located)which includes all information that is associated within a circular areadefined by the coordinates of the center of the intersection and pointswithin a specified radius of the center. In some embodiments, geographiclocation information may consist of numerous alternative routes avehicle 208 may travel to reach a selected destination.

Accident information may comprise information about whether a vehicle208 was in an accident. In some aspects, accident information mayidentify damaged parts of the vehicle 208 resulting from the accident.For example, accident information may detail that the front bumper,right door, and right front headlight of the vehicle 208 were damaged inan accident. In some examples, accident information may detail the costof replacement or repair of each part damaged in an accident. In someinstances, accident information may include previously described vehicleinformation. In some embodiments, accident information may include dataabout the location of the accident with respect to a road segment wherethe accident occurred. For example, accident information may includewhere the accident occurred on the road segment (e.g., which lane), thetype of road the accident occurred on (e.g., highway, dirt, one-way,etc.), time of day the accident occurred (e.g., daytime, night time,rush hour, etc.), and the like.

Some additional examples of accident information may include loss type,applicable insurance coverage(s) (e.g., bodily injury, property damage,medical/personal injury protection, collision, comprehensive, rentalreimbursement, towing), loss cost, number of distinct accidents for thesegment, time relevancy validation, cause of loss (e.g., turned leftinto oncoming traffic, ran through red light, rear-ended whileattempting to stop, rear-ended while changing lanes, sideswiped duringnormal driving, sideswiped while changing lanes, accident caused by tirefailure (e.g., blow-out), accident caused by other malfunction of car,rolled over, caught on fire or exploded, immersed into a body of wateror liquid, unknown, etc.), impact type (e.g., collision with anotherautomobile, collision with cyclist, collision with pedestrian, collisionwith animal, collision with parked car, etc.), drugs or alcoholinvolved, pedestrian involved, wildlife involved, type of wildlifeinvolved, speed of vehicle at time of incident, direction the vehicle istraveling immediately before the incident occurred, date of incident,time of day, night/day indicator (i.e., whether it was night or day atthe time of the incident), temperature at time of incident, weatherconditions at time of incident (e.g., sunny, downpour rain, light rain,snow, fog, ice, sleet, hail, wind, hurricane, etc.), road conditions attime of incident (e.g., wet pavement, dry pavement, etc.), and location(e.g., geographic coordinates, closest address, zip code, etc.) ofvehicle at time of incident.

Accident information associated with vehicle accidents may be stored ina database format and may be compiled per road segment and/or risk map.One skilled in the art will understand that the term road segment may beused to describe a stretch of road between two points as well as anintersection, roundabout, bridge, tunnel, ramp, parking lot, railroadcrossing, or other feature that a vehicle 208 may encounter along aroute.

FIG. 3 illustrates a computing system 300 for generating a contextualrisk assessment based on sensor information. Computing system 300 mayinclude a contextual risk assessment system 302, sensor(s) 304, mobilecomputing device 306, and vehicle 308. In some embodiments, computingdevice 101 shown in FIG. 1 may comprise the contextual risk assessmentsystem 302. In other embodiments, contextual risk assessment system 302may comprise or be similar to the previously described computing devices101, 141, or 151. In some aspects, sensor(s) 304 may be similar to thepreviously described sensor(s) 204 a-h. For example, sensor(s) 304 maybe coupled to or otherwise in communication with a vehicle 308. In someinstances, vehicle 308 may be similar to the previously describedvehicle 208. In some examples, a user mobile computing device 306 may besimilar to the previously described computing devices 101, 141, or 151.

The contextual risk assessment system 302 may comprise variousmodules/engines for generating a risk assessment value. Machine-readableinstructions may include one or more instruction modules 302, 310, 312,314, 316 and 320. The instruction modules may include computer programmodules. For example, a contextual risk assessment system 302instruction modules may include one or more of a data retrieval engine310, risk assessment engine 312, a driver instruction/engagement engine314, an information management system 316, risk map generation engine318 and optionally, machine learning module 320.

The data retrieval engine 310 may request or receive information fromother computing devices and sensors. For example, a data retrievalengine 310 may receive sensor information from sensor(s) 304, data frommobile computing device 306, and/or instructions/data from a user deviceor network device (not shown). A data retrieval engine 310 may receivedifferent types of sensor information as those previously described. Forexample, a data retrieval engine 310 may obtain environmentalinformation, vehicle information, weather information, and the like. Insome aspects, a data retrieval engine 310 may receive and use the sensorinformation (e.g., x-plane information, y-plane information, and z-planeinformation) to determine whether a vehicle 308 is moving up or down.

The risk assessment engine 312 may receive vehicle sensor informationdata, mobile device sensor information data and utilize the informationdata to complete different tasks. For example, the contextual riskassessment system 302 may receive sensor information from sensor(s) 304,and communicate with (e.g., transmit to and receive from) mobilecomputing device 306 in order to generate a risk map. In anotherexample, the contextual risk assessment system 302 may use receivedinformation and contextual risk assessment system modules to developalerts and recommendations, which may help to alert a driver ofpotential risks and with the use of the mobile device. A risk (e.g.,potential risk) may comprise anything that may create a dangerousdriving condition or increase a likelihood of a vehicle 308 getting intoan accident. A risk map created by risk map engine 318 may comprise animage (e.g., JPEG, TIFF, BMP, etc.), a video (e.g., MPEG), a hologram,or other visual outputs for illustrating a road segment or route beingtraveled by a vehicle 308. The risk map may further include markers orother indicators of risks (e.g. risk objects).

Risks may be any item, event, or condition that may pose a danger to avehicle 308 while the vehicle 308 is on a trip. In various embodiments,the risk map may be a two-dimensional (2D) or a three-dimensional (3D)illustration. Further, in some embodiments, the risk map may dynamicallychange over time. The changes may be in accordance with geographic dataindicating the vehicle's location and/or other data (e.g., speed dataindicating a speed of the vehicle or odometer data indicating distancethe vehicle traveled). In some embodiments, the risk map may be keyed orcoded (e.g., certain symbols, colors, and the like that representdifferent risks or categorize the risk objects within the risk map).

Referring to FIGS. 3 and 8, driving mode content activity risk matrix800 provide features that that can happen on a mobile device 306 (or themobile device 700 of FIG. 7) with driving tasks or tasks a user mightperform on the mobile device for a safe driving window. As shown in FIG.8, the “y-axis” or vertical axis of the driving mode content activityrisk matrix 800 indicates a driving mode, while the “x-axis” orhorizontal axis driving mode content activity risk matrix 800 indicatesconnected activity on the user's mobile device 306. During the drivingmode various connected activities may be enabled or disabled, which aredescribed in greater detail below, for example, with respect to FIGS.9A-9C. Risk assessment engine 312 may use data inputs in combinationwith risk map engine 318 to arrive at an overall contextual risk scoreso as to enable and disable certain driving and user mobile deviceactivities based on the risk score and provide a dynamically updatedcurrent risk score. In another implementation, risk assessment engine312 may predict future risk scores (e.g., 30 seconds ahead, 1 minuteahead, 5 miles ahead) as to enable driving and mobile device activitiesas to enable and disable certain driving and mobile device activities.For example, the user interface of FIG. 13 shows an overall contextualrisk assessment 1300, based on current information, and a future riskscore 1302, e.g., 30 second ahead.

The driver engagement engine 314 may generate recommendations of themobile device 306 to interface to provide visual, audio, or hapticfeedback. In some aspects, the driver engagement engine 314 maydetermine how to display a recommendation on the display screen of themobile device 306. In one example, as shown in FIG. 7, mobile device 700includes a display screen 726 may indicate an upcoming predicted safedriving condition with a “green” color indication. In another example, a“yellow” color screen may indicate an upcoming predicted cautiousdriving maneuver. In yet another example, a display screen 722 mayindicate an upcoming predicted safe driving condition with a “green”color indication, and then dynamically adjust to a “yellow” color screento indicate an upcoming predicted cautious driving maneuver. Forexample, if the system 302 knew the person's intended route, system 302could calculate a forecast of it for the entire route, and indicate ifthe user wants to make a phone call, the safe operation window is notnow, but in 10 minutes. The system 302 could forecast that the vehiclewill be merged onto an express way and pass a traffic jam at 10 minutes,so then the phone call would be “safer” (e.g., less risky) in thefuture. In another implementation of the forecast scenario, system 302may collect that information and can automatically make the call in 10minutes. That is, the call can be automatically established in 10minutes after the traffic hazard has passed.

In some aspects, the driver engagement engine 314 may determine how toprovide an audio recommendation via the speaker of the mobile device 306or over the vehicle 308 audio emanating from the mobile device 306. Inone example, while an audio music is playing, an audio announcement“voice over” could provide a safe turn recommendation or the audioannouncement could pause the music before playing the announcement. Insome aspects, the driver engagement engine 314 may determine how todisplay a recommendation on the display screen of the mobile device 306.In another aspect, the driver engagement engine 314 may enable the flooror seat of the vehicle 308 to vibrate or the mobile device 306 tovibrate for the haptic feedback engagement.

In some aspects, the recommendation from the driver engagement engine314 may be a color (or color change), an animation, a sound, avibration, and the like for indicating the level of risk associated withan upcoming driving activity or mobile device activity. For example, thedisplay screen may be displayed in yellow when a moderate risk to avehicle 308 is determined or displayed in red if it is a severe risk tothe vehicle 308. In another example, one sound may be played for a lowrisk while a different sound may be played for a high risk. In anotherexample, if a risk is identified as being located at the front of thevehicle 308, then the floor or seat of the vehicle 308 may vibrate. Inanother example, if a risk is identified as being located at theback/rear of the vehicle 308, then the back of the seat(s) of thevehicle 308 may vibrate. In some examples, if a risk is identified asbeing located on the left side of the vehicle 308, then a sound may playout of the left speaker(s) of the vehicle 308. In some examples, if arisk is identified as being located on the right side of the vehicle308, then a sound may play out of the right speaker(s) of the vehicle308.

Information management system 316 may organize and store all theinformation the risk assessment system 302 generates, transmits, andreceives. System 316 may store training data sets and self-learning dataset for machine learning module 320.

Some aspects of various exemplary constructions are described byreferring to and/or using neural network(s). Machine learning module 320may be configured to electronically process with a machine deep learningcontroller. Various structural elements of neural network include layers(input, output, and hidden layers), nodes (or cells) for each, andconnections among the nodes. Each node may be connected to other nodesand has a nodal value (or a weight) and each connection can also have aweight. The initial nodal values and connections may be random oruniform. A nodal value/weight may be negative, positive, small, large,or zero after a training session with training data set. Computernetworks 203, 201 may incorporate various machine intelligence (MI)neural network 500 (see FIG. 5) features of available TENSORFLOW(https://www.tensorflow.org), KERAS (a component of Tensorflow), orNEUROPH software development platforms (which are incorporated byreference herein). Referring to FIG. 5, neural network 500 is generallyarranged in “layers” of node processing units serving as simulatedneutrons, such that there is an input layer 508, representing the inputfields into the network. To provide the automated machine learningprocessing, one or more hidden layers 509 with machine learning rulesets may process the input data. An output layer 511 may provide theresult of the processing of the network data.

With continued reference to FIG. 3, machine learning module 320 mayimplement deep learning machine learning techniques, such as techniquesimplementing a representation of learning methods that allows a machineto be given raw data and determine the representations needed for dataclassification. Deep learning may include a subset of machine learningthat uses a set of algorithms to model high-level abstractions in datausing a deep graph with multiple processing layers including linear andnon-linear transformations. In some examples, the deep learning networkof machine learning module 320 may train itself to identify “good”features for analysis. Using a multilayered architecture, such asnetwork 500 of FIG. 5, machine learning module 320 may employ deeplearning techniques to better process raw data. Examining data forgroups of highly correlated values or distinctive themes may befacilitated using different layers of evaluation or abstraction.

Deep learning ascertains structure may be employed in data setsdescribed herein using backpropagation algorithms which are used toalter internal parameters (e.g., node weights) of the deep learningmachine. Deep learning machines may utilize a variety of multilayerarchitectures and algorithms, and may process raw data to identifyfeatures of interest without the external identification.

In some implementations, machine learning module 320 may include a deeplearning engine in a neural network environment that includes numerousinterconnected nodes referred to as neurons. Input neurons, activatedfrom an outside source, may activate other neurons based on connectionsto those other neurons which are governed by the machine parameters. Aneural network may behave in a certain manner based on its ownparameters. Learning may refine the machine parameters, and, byextension, the connections between neurons in the network, such that theneural network behaves in a desired manner.

One implementation of machine learning module 320 may include deeplearning technology that may utilize convolutional neural networksegments data using convolutional filters to locate and identifylearned, observable features in the data. Each filter or layer of theconvolutional neural network architecture may transform the input datato increase the selectivity and invariance of the data. This abstractionof the data may allow the machine learning module 320 to focus on thefeatures in the data it is attempting to classify and ignore irrelevantbackground information.

Deep learning operates on the understanding that many datasets includehigh level features which include low level features. While examining animage, for example, such as computer system diagrams, rather thanlooking for an object, it is more efficient to look for edges which formmotifs which form parts, which form the object being sought. Thesehierarchies of features can be found in many different forms of datasuch as speech and text, etc. This type of data can be used from themobile device 306 camera (front-facing and/or rear-facing camera).

In some implementations, learned observable features may include objectsand quantifiable regularities learned by the machine during supervisedlearning. A machine provided with a large set of well classified data isbetter equipped to distinguish and extract the features pertinent tosuccessful classification of new data. A deep learning machine thatutilizes transfer learning may properly connect data features to certainclassifications affirmed by a human expert. Conversely, the same machinecan, when informed of an incorrect classification by a human expert,update the parameters for classification. Settings and/or otherconfiguration information, for example, can be guided by learned use ofsettings and/or other configuration information, and, as a system isused more (e.g., repeatedly and/or by multiple users), a number ofvariations and/or other possibilities for settings and/or otherconfiguration information can be reduced for a given example trainingdataset.

An example deep learning neural network may be trained on a set ofexpert classified data, for example. This set of data may build thefirst parameters for the neural network, and this would be the stage ofsupervised learning. During the stage of supervised learning, the neuralnetwork may be tested based on whether the desired behavior has beenachieved.

Once a desired neural network behavior has been achieved (e.g., amachine learning module 320 has been trained to operate according to aspecified threshold, etc.), the machine learning module 320 can bedeployed for use (e.g., testing the machine with “real” data, etc.).During operation, neural network classifications may be confirmed ordenied (e.g., by an expert user, expert system, reference database,etc.) to continue to improve neural network behavior. The example neuralnetwork may then in a state of transfer learning, as parameters forclassification that determine neural network behavior are updated basedon ongoing interactions. In certain examples, the neural network mayprovide direct feedback to another process. In certain examples, theneural network may output data that is buffered (e.g., via the cloud,etc.) and validated before it is provided to another process.

The engines/modules/systems 302, 310, 312, 314, 316, 318 and 320 may,individually or in combination, comprise one or more processors andmemory storage devices (as previously described with reference toFIG. 1) for executing instructions causing one or more computing devicesto execute the operations and/or tasks previously described with respectto engines/modules/systems 302, 310, 312, 314, 316, 318 and 320. Theengines/modules/systems 302, 310, 312, 314, 316, 318 and 320 and othermodules may implement application programming interfaces (APIs)containing functions or sub-routines which can be executed by anothersoftware system, such as email and internet access controls. The systemsand methods of the present disclosure can be implemented in varioustechnological computing environments including Simple Object AccessProtocol (SOAP) or in the Representational State Transfer (REST). RESTis the software architectural style of the World Wide Web. REST APIs arenetworked APIs that can be published to allow diverse clients, such asmobile applications, to integrate with the organizations softwareservices and content. Many commonly-used applications work using RESTAPIs as understood by a person of skill in the art.

A user device such as mobile phone device 306, 700 may be used to senseuser information of the driver or other user's mobile devices in thevehicle 308. Referring to FIG. 13, this “mobile device derived inputdata” may be used to determine one or more contextual risk assessments1300 from a range of mobile device 306, 700 derived inputs to calculateone or more indexes, such as a tailgating risk index 1002, a roadfrustration index 1004, a lane keeping index 1006, a driver attentionindex 1008, an internal distraction index 1012 or other risk index (seeFIGS. 11-13). In one implementation, the use of mobile device 306, 700cameras can capture data to assess risks (both forward-facing camera andselfie camera). In some implementations, the system may increase thesampling rate of each camera based on risk assessment factors. Therear-facing camera (selfie camera) may be employed to derive a driverattention index 1008. In this implementation, the internal movements inthe vehicle interior of the occupants may be monitored. In otherimplementations, the mobile device 306, 700 with a formulaic or machinelearning approach may determine from monitoring the rear-facing camerafactors, such as the number of passengers or occupants 1010, andinternal cabin distractions (e.g., pets, dropped object).

In another implementation, the system 302 may integrate input from eachof the mobile device 306, 700 cameras to adjust the operation of theother camera, e.g., if driver is fully attentive based using on theselfie camera and driver attention index, then the system emphasizescollection and analysis of data from the forward camera. In anotherimplementation, if forward camera shows a risky situation based one ofmore of a tailgating index 1002, road frustration index 1004, or lanekeeping index 1006, then the system 302 uses (e.g., initiates,activates, enables, or the like) selfie camera to measure the driverattention index 1008 more closely. In some implementations, the usemobile device 306, 700 microphone may be employed to determine aninternal distraction index derived from sounds that might indicateunusual distractions inside the vehicle such as boisterous activity(e.g., teens getting out of control), a crying baby, dog barking,shouting; namely sounds of activity that could lead to distracteddriving.

In various operations the risk assessment system 302 provides for a“safe operation” window for dynamically updating the contextual riskassessment. In some implementations, the system 302 may enable ordisable certain features at traffic intersections because intersectionscan be an area of a higher risk for accidents. The system 302 withforward facing camera may detect traffic light 402 and intersection 400,such as shown in FIG. 4, thus enabling/disabling certain features of thevehicle and mobile device so as encourage drivers to focus on driving,instead of high risk activities (e.g., texting or other mobile deviceactivities). In some implementations, the system 302 permits a widerange of connected life activities while the vehicle is stopped at atraffic light 402. The system 302 can determine a traffic light statusby combining and analyzing various data inputs, mobile device 306, 700sensor data, such as GPS data, accelerometer data, image data from theforward facing camera and/or risk data from risk map engine 318.

In some implementations, system 302 can use the forward facing camera ofmobile device 306, 700 to determine activity around a traffic light 402such as when the traffic light changed color, the presence of stoppedcar in front of the vehicle, detect movement of car in front (followinglight changing to green). In one implementation, a machine learningmodule 320 of system 302 can build a “self-learning” data set in system316 that estimates the duration of a traffic light and may collect dataon time between light changes for each intersection; to find patterns toprovide enhanced recommendations. In another implementation, system 302with module 320 can estimate time available at a traffic light based onfactors such as the self-learning data set (which indicates typicalduration of a light sequence), timing of traffic light change ascaptured by the forward-facing camera, when the time vehicle stops. Inother implementations shown in FIG. 9C, system 302 can provide a“countdown clock” based on the estimated time until the traffic lightchanges and enable the connected life activity during this window.

In some implementations, system 302 can determine an end of “connectedlife activity” window based on one or more of: an expiration ofcountdown clock, movement of the vehicle, or movement of vehicle aheadof the vehicle. In some implementations, system 302 once the vehiclestarts moving, system 302 can suspend connected life activity (e.g.,disable/enable functionality) until a next safe window (e.g., whenstopped at the next traffic light) In some implementations, system 302can invoke a suspension mode to enable rapid, efficient resumption whennext safe window opens (i.e., pick up where the system finished leftoff).

In various operations the risk assessment system 302 can provide a “safeoperation” window for dynamically updating the contextual riskassessment using risk map engine 318 to determine what, if any, specialturn lanes and signals exist at an intersection. In one use caseexample, vehicle stopping time at a traffic light can vary depending onwhat maneuver a driver takes at the intersection. Left turns can besubject to turn arrows that are on a different timing sequence than,“straight thru” traffic lights. Right turns might require a full stopafter which the motorist can make the right turn when it is safe to doso (assuming there is not a “no right turn on red” sign). In oneimplementation, system can determine what maneuver a driver intends atthe intersection and set the “countdown” clock according to the expectedwait time for that maneuver. For instance, if the driver haspredetermined specified a route (rather than simply driving withouthaving specified destination and therefore an intended route), thenavigation functionality of the system 302 would know that the driverintends to make a left turn. In this case, system 302 can set thecountdown clock based on the timing sequence that applies to the leftturn signal.

In another implementation of system 302, if the driver has not specifieda route, the forward-facing camera of mobile device 306 may determinethat the driver is in the left-turn lane and select the timing sequencethat applies to the left turn signal. This determination may be made byone of the following or several factors in combination: a forward facingcamera, machine vision traffic signage recognition, machine vision lanemarkings recognition, and/or determining actions taken by motoristsahead of or alongside the ego vehicle (for instance, are they signalingand taking left turns). This may help determine what lane the motoristis in (left turn, straight, right turn).

FIG. 6 illustrates a flowchart 600 for an operation for risk assessmentand a recommendation that may be provided to a user. The operation maybegin at step 605. At step 605, a risk assessment system 302 may receivesensor information from various sensors (e.g. sensor(s) 304 and mobiledevice sensors). In some aspects, the risk assessment system 302 maystore the received sensor information. In some examples, the sensorinformation may be the same as the sensor information previouslydescribed. In some embodiments, the risk assessment system 302 mayreceive sensor information from mobile computing device 306, a networkdevice, or other computing devices. After step 605, the method mayproceed to step 610.

At step 610, a risk assessment system 302 may receive geographicinformation (e.g., geographic location information). In someembodiments, the risk assessment system 302 may receive the geographiclocation information as part of the sensor information. In some aspects,the risk assessment system 302 may receive the geographic locationinformation from a GPS device installed in a vehicle, mobile computingdevice 306, or another computing device. For example, in step 610, therisk assessment system 302 may receive GPS coordinates from a mobilephone of a driver of a vehicle via a cellular backhaul. In someinstances, the geographic location information may comprise the samegeographic location information as previously described. For example,the geographic location may comprise latitude and longitude coordinatesof vehicle 308. After step 610, the method may proceed to step 615.

At step 615, the risk assessment system 302 may receive environmentalinformation. In some embodiments, the risk assessment system 302 mayreceive the environmental information from a mobile computing device 306or another computing device located in or on the vehicle. Additionally,or alternatively, the risk assessment system 302 may receiveenvironmental information from a network device (e.g., a third partyserver, such as a server of a weather reporting organization). In someinstances, the network device may be a computing device or a serversimilar to the risk assessment system 302, and may be operated by aninsurance company. In some examples, the network device may containenvironmental information about various routes, various road segments,various risk objects, and/or various risk maps that may be related to aroute that a vehicle 308 may be traveling along. In some instances, theenvironmental information may comprise data about routes, road segments,risk objects, and/or risk maps. In some examples, the environmentalinformation may be similar to environmental information previouslydescribed. In other embodiments, the risk assessment system 302 mayreceive environmental information from another computing device (system)associated with a different vehicle. In another embodiment, the riskassessment system 302 may receive environmental information regarding aroad segment, via a computing device responsible for tracking theconditions of the road segment. In some embodiments, the risk assessmentsystem 302 may receive environmental information from astructure/infrastructure (e.g. building, bridge, railroad track, etc.)via a computing device configured to monitor the structure. After step615, the method may proceed to step 620.

At step 620, the risk map engine 318 may generate a risk map based onthe received information, individually or combined, from steps 605, 615,and 620. In some embodiments, the risk assessment system 302 maygenerate a risk map using sensor information. In some aspects, the riskassessment system 302 may analyze and manipulate the sensor informationin order to create images, figures, and the like that represent a map oran environment that a vehicle 308 may be traveling through. In someinstances, the manipulation of sensor information may compriseinterpreting input information (e.g., sensor information) andreconstructing the sensor information in a way it may be used togenerate an image, an object, or a virtual environment. In some aspects,the risk map generation system 302 may generate a risk map using riskobjects, risk values, road segments, and/or other risk maps. In someembodiments, risk assessment system 302 may generate a risk map usingroad segments, risk objects, and risk maps received from other computingdevices. In view of this disclosure, it will be understood that the riskmap may be generated in various ways. After step 620, the method mayproceed to step 625.

At step 625, the risk assessment system 302 may analyze vehicleinformation data, including sensor data and mobile device derived inputsto calculate one or more indexes, such as a tailgating risk index, aroad frustration index, a lane keeping index, a driver attention index,an internal distraction index or other risk index, the risk map andcalculate a risk value. In some aspects, the risk value may berepresented by a risk score. In some embodiments, the risk assessmentsystem 302 may calculate the risk value for a predicted driving activityby applying actuarial techniques to the sensor information that may bereceived from sensors 304 and mobile device 306 (such as rear-facing andforward facing camera, GPS, and accelerometer). In other examples,system 302 may calculate the risk value by applying actuarial techniquesto the information that may be received by the risk map engine 318. Insome embodiments, the risk assessment system 302 may calculate the riskvalue based on the likelihood of proposed activity causing the vehicleto get in an accident. In some aspects, the risk assessment system 302may calculate the risk value using only road information. For example,the risk value may be calculated based on only the physical attributesof the road. In some embodiments, the risk assessment system 302 maycalculate the risk value using only the sensor information. In someinstances, risk assessment system 302 may analyze a risk to calculate arisk value as previously described with regards to FIG. 3.

After calculating a risk value at step 625, the risk assessment system302 may associate the road segment a vehicle is traveling on or useractivity to the risk value at step 630. In some aspects, associating therisk value may include analyzing the road segment or risk map thevehicle 308 is traveling along or through, identifying one or more riskobjects and the characteristics of the one or more risk objects, andcalculating a risk value based on the number of risk objects and thecharacteristics of the one or more risk objects located on the roadsegment or within the risk map. In some aspects, the risk assessmentsystem 302 may have a list of pre-determined risk objects correlated toa predetermine risk value. In some aspects, the risk assessment system302 may have different groupings of user risk activity (which may becategorized by the characteristics of the risk activity) correlated topredetermined values. In some embodiments, a user may be able todetermine, categorize, or correlate a user risk activity to a userselected value. In some embodiments, a specially configured orprogrammed server or computing device of an insurance provider thatmanages the computing system may rank, prioritize, or correlate the userrisk activity to a selected value. For example, a risk user activityspeaking on a mobile phone may have a risk value of 10, while risk useractivity of holding and texting with mobile phone while on the road mayhave a risk value of 20. In some aspects, the risk value assigned mayrepresent the likelihood of a risk activity causing an accident. Afterstep 630, the method may proceed to step 635.

At step 635, the risk assessment system 302 may assemble risk data intomultivariable equations. For example, the risk assessment system 302 mayuse the data (identified at step 630) to determine a risk value of anobject or a segment of road or user activity with mobile device based onpredetermined equations, such as input from a tailgating risk index1002, a road frustration index 1004, a lane keeping index 1006, a driverattention index 1008, an internal distraction index 1012 or other riskindex. The equations may be configured for different information inputswhich may affect the risk value assigned to a risk object, risk map,and/or road segment. For example, one equation may use sensor data whileanother equation may use the sensor data, environmental data, andgeographic location data to determine a risk value. In some instances, anetwork device or insurance provider's server may generate and determinethe multivariable equations. In some embodiments, the multivariableequations may be generated using actuarial techniques. Once the riskassessment system 302 assembles the received information intomultivariable equations or machine learning module 320, the method mayproceed to step 640.

At step 640, the risk assessment system 302 may calculate a modifiedrisk value based on environmental information and/or other receivedinformation. For example, the risk assessment system 302 may use thedetermined risk values from step 625 and use the multivariable equationor machine learning module 320 from step 635 to use additional receiveddata (e.g., geographic location information and/or environmentalinformation) to calculate a modified risk value. As another example, arisk value determined at step 625 may be adjusted. Under this example,the risk assessment system 302 may adjust a risk value due to a newcondition (e.g. snow on the road). Due to the snow, risk assessmentsystem 302 may use the multivariable equation or machine learning module320 to determine that the previous risk value needs to be modified. Uponcompletion of step 640, the method may proceed to step 645.

At step 645, the risk assessment system 302 may store the modified riskvalue in memory. In some aspects, the modified risk value may becorrelated to mark or enhance a particular risk object, road segment,and/or risk map. The particular risk object, road segment, or risk mapmay be updated and assigned the new modified risk value. The updatedrisk object, road segment, or risk map with its updated risk value maybe stored by the risk assessment system 302 into a database. In someaspects, the database information may be shared with other computingdevices or be used to generate other risk maps with similar road segmentcharacteristics. After step 645 is completed, the method may proceed tostep 650.

At step 650, the risk assessment system 302 may determine if the riskvalue (e.g. risk score) is over a threshold. The threshold value may bedetermined by a user or a system provider (e.g., insurancecompany/provider). Also, the threshold value may be adjusted on acase-by-case basis (e.g., driver by driver basis), or may bepredetermined. If the risk assessment system 302 determines that therisk value is not over the threshold, the method may proceed to step605. If the risk assessment system 302 determines that the risk valuehas exceeded the threshold, then the method may proceed to step 655.

At step 655, the risk assessment system 302 may update the riskassessment based on the modified risk value. In some aspects, updatingthe risk assessment may comprise changing screen colors and/or audioindication. The indicators may function to help alert the driver ofpotential risks and/or disable certain features of the mobile device. Insome examples, the indicator may result in enhancing a risk with acolor, a sound, or an animation. In some instances, an indicator issimilar to the indicator as described with reference to FIG. 3. Afterstep 655 has completed, the method may proceed to step 660.

At step 660, the risk assessment system 302 may alert the user of avehicle or provide recommended course of action to avoid the riskysituation. The recommended course of action may be any previouslydescribed indicator by driver engagement engine 314 (with reference toFIG. 3). In some aspects, the recommended course of action may identifyor indicate to the user of a vehicle that there is a potential risk andwhat that potential risk may be. In some examples, the risk assessmentsystem 302 may display the risk object, road segment, and/or risk map toa user. In other examples, risk assessment system 302 may provide theuser with an audio alert. In other examples, the mobile device 306 andsoftware logic flash type program memory 714, for storage of variousprogram routines and configuration settings and has APIs that interfacewith driving engagement engine 314 to engage or disable features ofmobile device 306. Using risk matrix 800, if the risk assessment isconducted in driving mode cruising, then the mobile device 306 can allowanswering the call. Still using risk matrix 800 shown in FIG. 8, if therisk assessment is conducted in driving mode maneuver, then the mobiledevice 306 would not allow searching, texting, answering incoming calls,for example, as the APIs would interact with the internal phone logic.After step 660 has completed, the method of FIG. 6 may be repeated sothat another risk assessment may be generated or so that the riskassessment is continually and/or dynamically updated to as the vehiclemoves.

While the above steps described an example method or operation inaccordance with FIG. 6, steps may be added, omitted, or modified to themethods of the FIG. 6 without departing from the scope of the presentdisclosure.

FIG. 7 provides a block diagram illustration of an exemplary user device700. Although the user device 700 may be a smart-phone, a tablet, oranother type of device, the illustration shows the user 700 is in theform of a handset (although many components of the handset, such as amicrophone and speaker, are not shown).

Referring to FIG. 7 for digital wireless communications, the user device700 includes at least one digital transceiver (XCVR) 708 connected to anantenna 710 that receives data packets uploads and downloads using celltower transmissions with from an Over-The-Air Transmitter. Thetransceiver 708 provides two-way wireless communication of information,such as digital information, in accordance with the technology of thenetwork. The transceiver 708 also sends and receives a variety ofsignaling messages in support of the various services provided via theuser device 700 and the communication network. The user device 700 alsoincludes an NFC interface 709 having an associated antenna andconfigured for communicating using near-field communication with otherdevices such as with an NFC reader. The user device 700 includes adisplay 722 for displaying messages, menus or the like, user relatedinformation for the user, etc. A touch sensor 726 and keypad 730 enablesthe user to generate selection inputs, for example.

A microprocessor 712 serves as a programmable controller for the userdevice 700, in that it controls all operations of the user device 700 inaccord with programming that it executes, for all normal operations, andfor operations involved in the real-time parking guidance service. Inthe example, the user device 700 includes flash type program memory 714,for storage of various program routines and configuration settings. Theuser device 700 may also include a non-volatile random access memory(RAM) 716 for a working data processing memory. In a presentimplementation, the flash type program memory 714 stores any of a widevariety of applications, such as navigation application software and/ormodules. The memories 714, 716 also store various data, such as input bythe user. Programming stored in the flash type program memory 714 isloaded into and executed by the microprocessor 712 to configure theprocessor 712 to perform various desired functions, including functionsinvolved in push notification processing. In some examples, the userdevice 700 further includes a GPS interface 711 coupled to a GPS antennadesigned to receive GPS location signals transmitted by satellites. TheGPS interface 711 is communicatively coupled to the microprocessor 712,and is operative to provide location information to the microprocessorbased on the received location signals. Microphones and front-facing andrearing facing cameras (not shown in FIG. 7) may also be provided withuser device 700.

FIGS. 9A-9C depicts illustrative user interfaces in accordance withvarious implementations of driver engagement engine 314. In FIG. 9A, thedriving mode has a pending maneuver, such as a left turn on street“Ontario” in 200 yards. The display screen 722 may have color indicationof a yellow color. Contextual risk assessment system 302 might not allowpermitted mobile user activities during a maneuver window due to theincreased risk assessment at the intersection. In FIG. 9B, the drivingmode indication shows a safe cruising mode. During this mode, the screendisplay 726 may have a color indication of a green color. Contextualrisk assessment system 302 may allow some permitted activities duringsafe cruising, such as music selection and control. In FIG. 9C, the modeindication shows a traffic light mode. During this mode, the displayscreen 722 may have a color indication of a green color. Contextual riskassessment system 302 may allow most permitted activities during thisstopped situation and may, e.g., enable the user to select music, searchand select destinations, search and select news headlines, initiatetelephone call, and dictate and send text messages. The screen displaymay provide an indication of the estimate stop time.

In one user scenario, if a motorist drives in a car with others to adestination on the open highway in light traffic (e.g., roadconditions), the system 302 may enable the motorist to be able to, atleast, select music to play. In another case, on that same drive, thesystem 302 may determine whether the vehicle is currently in a safeoperation mode to do a destination selection for a gas station up aheador a place to eat up ahead on the road. The system 302 may enable thosefeatures where the risk assessment is low and safe.

Referring to FIG. 11, the tailgating index 1002 is scaled from high tolow. In some implementations, the mobile device 306 and software logicflash type program memory 714, for storage of various program routinesand configuration settings and has APIs that interface with drivingengagement engine 314. In one operation, the front facing camera ofmobile device 306 take a digital image 1100 of the scene in front of thevehicle 308. The closer the car in is vehicle 308, then the tailgatingindex 1002 is scaled high. If the car is not close in with predetermineddistance from the vehicle 308, then the index 1002 goes lower. Stillreferring to FIG. 11, the road frustration index 1004 is scaled fromhigh to low. In one operation, the front facing camera of mobile device306 take a digital image 1100 of the scene in front of the vehicle 308.The number of vehicles on the road, such as rush hour traffic congestioncan be used to calculate the index 1004. Machine learning module 320,while examining an image 1100 may look for edges which form motifs whichform parts, which form the object being sought, such multiple cars theroad in front of vehicle 308. The more cars detected in the image 1100,then the index 1004 can go higher in the scale. Likewise, if fewer carsare detected in the image 1100, then index 1004 scales lower. Lanekeeping index 1006 indicates that number of events of sudden lateralmovements of the vehicle 308 with the accelerometer of the mobile device306. Machine learning module 320, while examining an image 1100, maylook for edges which form motifs which form parts, which form the objectbeing sought, such as side lanes of the road that the vehicle istraveling.

Referring to FIG. 12, the driver attention index 1008 is scaled on asliding scale from alert at one end to distracted at another end. Insome implementations, the mobile device 306, 700 may include softwarelogic flash type program memory 71, for storage of various programroutines and configuration settings and may have APIs that interfacewith driving engagement engine 314. In one operation, the rear facingcamera of mobile device 306 may take a digital image 1200 of the scenein the interior cabin of the vehicle 308. Machine learning module 320,while examining an image 1200, can look for edges which form motifswhich form parts and/or which form the object being sought, such as theinterior of the vehicle 308 to find sudden movement of the occupants.Internal distractions index 1012 may be scaled from low to high.Microphone data of the mobile device 306 may be monitored to createindex 1012. High pitch sounds and/or loud noises may determine an indexvalue using formulaic or machine learning module 320. Referring to FIG.13, the current contextual risk assessment 1300 is scaled from high tolow.

Referring to FIG. 13, mobile device derived input data may be used todetermine one or more contextual risk assessments 1300 from a range ofmobile device 306, 700 derived inputs to calculate one or more indexes,such as a tailgating risk index 1002, a road frustration index 1004, alane keeping index 1006, a driver attention index 1008, an internaldistraction index 1012 or other risk index (see FIGS. 11-13) using oneor more of multivariable equations of machine learning module 320 orother described herein.

In some implementations, the system 302 may automatically place thephone in “safe driving mode” if it is not inserted into a dockingstation to enable hands free operation. This operation may help ensurecompliance/usage of that docking station for hands free operation of themobile phone. In one implementation, the docking station may have asmart sensor to determine presence of phones in the station. The phonemay then determine who is driving based on the rear-facing camera(selfie camera). The mobile device may connect to the vehicle 308 via,Wifi, Bluetooth, NFC, or other wireless method.

In some implementations, the system 300 may self-determines(using—machine learning module 320) when it is unable to adequatelysurvey and assess surrounding conditions. When it is unable to surveyand assess, the system may undertake one or more of several actionsincluding, but not limited to: alerting the driver that the detectioncapabilities have been suspended; or automatically placing the phoneinto a “safe driving” mode (e.g., automatically limits “connected life”functions to a minimum). In other implementations, the samefunctionality may be activated if the phone's position is changed (e.g.,gets dislodged such that it no longer is pointed correctly at the roador into the cabin), or somehow fails to function properly.

In one mode of operation, the phone (e.g., user device 700) may beplaced into a docking station to activate a safe-driving mode. That is,the docking station may be able to detect whether the phone (e.g., userdevice 700) is in the car but is not in the docking station to activatethe mode. In another mode of operation, the phone may be in the dockingstation and activated and working, but upon a change in conditions suchthat the phone cannot work normally, the docking station may activate asafe default mode. Then the system may provide performance and signalback to the user in the safe default mode.

Referring to FIG. 2, during operation of the vehicle 208, a user (e.g.,driver, passenger, etc.) of the vehicle 208 may be located at theposition depicted by identifier 202. Sensors 204 (e.g. 204 a through 204h) may be located inside, outside, on the front, on the rear/back, onthe top, on the bottom, and/or on each side of the vehicle 208. In somecases, the number of sensors 204 used and positioning of the sensors 204may depend on the vehicle 208, so that sensor information for all areassurrounding the vehicle 208 may be collected. The sensors 204 (e.g. 204a through 204 h) may lens or a camera system to read light datasurrounding the vehicle.

In one implementation, a camera system with car sensors 204 a-204 h maybe configured with depth perception (e.g., stereo vision) so that it maycalculate different depths level in the field of view. Oneimplementation of machine learning module 320 may include deep learningtechnology that may utilize convolutional neural network segments datausing convolutional filters to locate and identify learned, observablefeatures in the data in the field of view. In one mode of operation, thesystem with module 320 may calculate a perception of low lightconditions, nighttime dark conditions (e.g., dark roads), excess lightglare conditions (e.g., sun light glare) or partially observableenvironment (e.g., fog or snow) to determine whether the visualenvironment is degraded by one or more percent levels. Then at apredetermined threshold, the system may activate a safe default mode. Inone example, a lens of the camera system might not have full view and,in a multi lens camera system, a predetermined threshold of percentdegraded may result in alerting the driver and going into a safe defaultmode. In this way, using a visibility degradation level based on currentsituation, certain functions may be activated or deactivated for a safedefault mode.

In some implementation, mobile computing device 306 with cameras(front-facing and/or rear-facing camera) may be configured with depthperception (e.g., stereo vision) so that it may calculate differentdepths level in the field of view. One implementation of machinelearning module 320 may include deep learning technology that mayutilize convolutional neural network segments data using convolutionalfilters to locate and identify learned, observable features in the datain the field of view of the cameras. In one mode of operation, thesystem with module 320 may calculate a perception of low lightconditions, nighttime dark conditions (e.g., dark roads), excess lightglare conditions (e.g., sun light glare) or partially observableenvironment (e.g., fog, heavy rain, or snow) to determine whether thevisual environment is degraded by one or more percent levels. Then at apredetermined threshold level, the system 300 may activate a safedefault mode. In one example, a lens of the camera system might not havea full view or may be obstructed, and, in a multi-lens camera system, apredetermined threshold of percent degradation may result in alertingthe driver and entering into a safe default mode. In this way, using avisibility degradation level based on current situation, certainfunctions may be activated or deactivated for a safe default mode.

In some implementations, referring to FIGS. 3 and 8, driving modeconnected activity risk matrix 800 may provide features that that canhappen on a mobile device 306, 700 with driving tasks or tasks a usermight perform on the mobile device for a safe driving window for thesafe default mode, including predictive analysis with machine learningmodule 320 using cameras (front-facing and/or rear-facing camera). The“y-axis” or vertical axis may indicate a driving mode, while the“x-axis” or horizontal axis may indicate connected activity on theuser's mobile device 306. One implementation of machine learning module320 may include deep learning technology that may utilize convolutionalneural network segments data using convolutional filters to locate andidentify learned, observable features in the data so that during thedriving mode, various connected activities could be enabled or disabledbased on the current driving conditions or predicted driving conditions.

In one use case example, the system 300 with machine learning module 320can be predictive about upcoming driving conditions using traffic datainput and weather data input (see FIGS. 6 and 8). In someimplementations, the risk assessment system 302 and machine learningmodule 320 may receive sensor information from mobile computing device306, a network device, or other computing devices. The risk assessmentsystem 302 and module 320 may receive geographic information (e.g.,geographic location information) from a GPS device installed in avehicle, mobile computing device 306, or another computing device. Forexample, the risk assessment system 302 may receive GPS coordinates froma mobile phone of a driver of a vehicle via a cellular backhaul. Forexample, the geographic location may comprise latitude and longitudecoordinates of vehicle 308 or mobile computing device 306.

In one implementation, the system 300 has a risk map generation engine318 which can predict roads or normal travel ahead of the vehicle 308.Based on the navigational GPS directions of vehicle 308 (with mobiledevice 306), the system 300, can predict the sun angle and sunrisetiming in a particular geographic area. In one implementation, thesystem 300 can advise when to start the user's playlist or make ahands-free call before the roads conditions degrade using predictiveanalysis with machine learning module 320. Using risk matrix 800, if therisk assessment is conducted in driving mode cruising, then the mobiledevice 306 alert the user to answer a call. Still using risk matrix 800shown in FIG. 8, if the risk assessment is for a predicted driving modemaneuver using module 320, then the mobile device 306 would not allowsearching, texting, answering incoming calls, for example, as the APIswould interact with the internal phone logic. Hence, in oneimplementation, the system 300 may provide the user with the ability toaccomplish what they need to accomplish before they enter into thatparticular situation for making a hands-free phone call or other taskwith the mobile device 306 (to safely execute in safe road conditions).

In some implementations, the system 300 may use the past history of thefull usage (stored in computer readable medium/database) to learn andpredict (using module 320) when the users might use the mobile device306 during a driving operation. For example, a user might tend to havemore calls at a certain time window during driving to the work or anoffice or other destination. By using risk matrix 800 shown in FIG. 8,if the risk assessment for a predicted driving mode maneuver usingmodule 320 involves higher risk, then the mobile device 306 would notallow searching, texting, answering incoming calls. In anotherimplementation, the system 300 could store the types history of theactivations or applications of the during the same time of the dayduring the week. The system 300 can store day versus week and weekendand the time with the type of applications. If any of these activationscan involve higher predictive risk, the system 300 could alert the userto modify their driving behaviors to reduce risk, such as would notallow searching, texting, answering incoming calls during a predictivehigher risk driving operation.

In some implementations, the driver engagement engine 314 may determinehow to provide an audio recommendation via the speaker of the mobiledevice 306, 700 or over the vehicle 308 audio emanating from the mobiledevice 306, 700. In some implementations, system 300 may have a naturallanguage processor or keyword recognition software or machine learningmodule 320 to read text messages that would be highly relevant to theuser. In one example, an SMS text message may include, in the header ofthe message or in the body of the text as a keyword as “urgent, callme”. The system 300 could interpret that as part of the message andalert the user verbally with module 314 that “you have an urgent call,you want me to place the call?” The user can provide verbal commandswithout a need to touch the mobile device 306. However, by using riskmatrix 800 shown in FIG. 8, if the risk assessment for a predicteddriving mode maneuver using module 320 involves higher risk, then themobile device 306 via drive engagement module 314 might not allowsearching, texting, answering incoming calls or placing calls until therisk is at safer level for the driving operation. In this way, thesystem 300 may enable initiation of a mobile device action when themobile device is in a safe driving environment, which would then protectthe user and reduce risks of driving, while using the mobile device isin a hands-free operation.

In some implementations, the system 300 via machine learning module 320may learn usage patterns such as how often a person accesses mobiledevice 306 so as to advise the user of safe driving behaviors. In oneexample, when the mobile device 306 is not in the docketing stations, anapplication may run in the background to log certain activities, suchthe frequency with which emails or texts messages are checked. Thesystem 300 may provide a user a verbal alert such as “you have no newmessages” in certain scenarios. In another implementation, the systemmay provide another verbal alert to the user, such as “when somethingcomes through and the system will alert you.” Such alerts may preventfrequent use of the mobile device 306 during driving conditions. In someimplementations, predicted interactions with the mobile device 306 maybe determined based on detected patterns of use, and, the mobile device306 enable different operations at the appropriate frequency or sequencefor safer driving based on such predicted interactions. In thisimplementation, the system 300, e.g., using risk matrix 800 shown inFIG. 8, may calculate the risk assessment for a predicted driving modemaneuver using machine learning module 320 and may then then enablecertain phone operations based on the risk assessment.

In some implementations, the system 300 via machine learning module 320could learn usage patterns, such as how often a person accesses withmobile device 306, so as to engage the user for safe driving behaviorsand provide predictive job queues for various phone actions during thesafe driving time. For example, based on the patterns the system 300could initiate calls based on verbal commands. The user can prearrangein system 300 that they want to call their work place or person, orlocal business prior to the driving operation.

The foregoing descriptions of the disclosure have been presented forpurposes of illustration and description. They are not exhaustive and donot limit the disclosure to the precise form disclosed. Modificationsand variations are possible in light of the above teachings or may beacquired from practicing of the disclosure. For example, where thedescribed implementation includes software, it should be understood thata combination of hardware and software or hardware alone may be used invarious other embodiments. Additionally, although aspects of the presentdisclosure are described as being stored in memory, one skilled in theart will appreciate that these aspects can also be stored on other typesof computer-readable media, such as secondary storage devices, like harddisks, floppy disks, or CD-ROM; a carrier wave from the Internet orother propagation medium; or other forms of RAM or ROM.

What is claimed is:
 1. A system comprising: one or more sensors coupledto a vehicle and configured to detect sensor information; and acomputing device in signal communication with the one or more sensors,wherein the computing device comprises: a processor; and memory storinginstructions that, when executed by the processor, cause the computingdevice to: communicate with the one or more sensors to receive thesensor information; communicate with a mobile device to receive mobileinformation; generate one or more dynamic risk values associated withoperation of the vehicle based on analyzing the sensor information andthe mobile information to determine at least one of a tailgating riskindex, a road frustration index based on a number of vehicles in frontof the vehicle, or a lane keeping index; generate a recommendation basedon the one or more dynamic risk values; transmit the recommendation tothe mobile device; and cause the recommendation to be displayed on aninterface of the mobile device.
 2. The system of claim 1, wherein thememory further stores instructions that, when executed by the processorcause the computing device to: receive additional information, whereinthe additional information includes at least one of additional sensorinformation or additional mobile information; and calculate one or moreupdated dynamic risk values by applying a machine learning model to theadditional information.
 3. The system of claim 1, wherein generating theone or more dynamic risk values includes generating at least one riskassessment associated with operation of the vehicle based on the sensorinformation and the mobile information.
 4. The system of claim 3,wherein the at least one risk assessment includes at least one of adriver attention index or an internal distraction index.
 5. The systemof claim 1, wherein generating the recommendation includes determiningwhether the one or more dynamic risk values exceeds a risk threshold. 6.The system of claim 1, wherein the recommendation comprises instructionsthat cause one or more features of the mobile device to be enabled ordisabled.
 7. The system of claim 1, wherein the recommendation comprisesa risk map, the risk map including one or more locational riskassessments.
 8. The system of claim 1, wherein the recommendationcomprises a calculated safe route.
 9. The system of claim 1, whereingenerating the one or more dynamic risk values includes accessing adatabase of risk scores associated with one or more aspects of vehicleoperation.
 10. The system of claim 1, wherein the mobile informationincludes at least one of: accident information, geographic information,environmental information, risk information, or vehicle information. 11.A method comprising: collecting sensor information from one or moresensors coupled to a vehicle; collecting mobile information from amobile device associated with an occupant in the vehicle; analyzing thesensor information and the mobile information; generating one or moredynamic risk values associated with operation of the vehicle based onanalyzing the sensor information and the mobile information to determineat least one of a tailgating risk index, a road frustration index basedon a number of vehicles in front of the vehicle, or a lane keepingindex; and disabling one or more features of the mobile device based onthe one or more dynamic risk values.
 12. The method of claim 11, furthercomprising generating a risk assessment associated with an operationparameter of the vehicle based on the sensor information and the mobileinformation.
 13. The method of claim 12, wherein generating the riskassessment includes generating a current risk assessment and a futurerisk assessment.
 14. The method of claim 11, further comprising:receiving additional information, wherein the additional informationincludes at least one of additional sensor information or additionalmobile information; and generating one or more updated dynamic riskvalues by applying a machine learning model to the additionalinformation.
 15. The method of claim 11, further comprising:transmitting a recommendation to the mobile device, wherein therecommendation is based on the one or more dynamic risk values and isconfigured to be displayed on an interface of the mobile device.
 16. Anapparatus comprising: a processor; a wireless communication interface;and memory storing instructions that, when executed by the processor,cause the apparatus to: receive, from one or more sensors coupled to avehicle, sensor information associated with operation of the vehicle;receive, from a mobile device, mobile information associated withoperation of the vehicle; generate one or more dynamic risk valuesassociated with operation of the vehicle based on analyzing the sensorinformation and the mobile information to determine at least one of atailgating risk index, a road frustration index based on a number ofvehicles in front of the vehicle, or a lane keeping index; transmit arecommendation to the mobile device, wherein the recommendation isgenerated based on the one or more dynamic risk values; and cause therecommendation to be displayed on an interface of the mobile device. 17.The apparatus of claim 16, wherein the memory stores furtherinstructions that, when executed by the processor cause the apparatusto: receive additional information, wherein the additional informationincludes at least one of additional sensor information or additionalmobile information; and generate one or more updated dynamic risk valuesby applying a machine learning model to the additional information. 18.The apparatus of claim 16, wherein the sensor information includes atleast one of a vehicles speed, a rate of acceleration, braking,steering, impacts to the vehicle, or usage of vehicle controls.
 19. Theapparatus of claim 16, wherein the sensor information includes vehicleoperational data collected by one or more internal vehicle sensors, oneor more computers, or one or more cameras.
 20. The apparatus of claim16, wherein the mobile information includes at least one of: accidentinformation, geographic information, environmental information, riskinformation, or vehicle information.